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Research questionHow can we reliably assess whether conversational LLMs clarify ambiguity and recover user intent?A clarification system must identify what is missing, ask useful questions, interpret the replies, and reach the user’s intended request. Evaluation is difficult because replies may be irrelevant or challenging, and success depends on both the dialogue and its final interpretation.
AI
Evaluation & Benchmarks
Multi-agent Systems
Natural Language Processing
Latest papersRecent research connected to this question, newest first.A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language ModelsThe source presents a tri-agent evaluation setup in which a question-clarifying agent is tested, a respondent agent simulates user replies, and an evaluator agent judges the dialogue. It describes synthetic supply-chain data, measures ambiguity handling, question quality, dialogue efficiency, language appropriateness, and intent alignment, and briefly discusses validation against human judgments.research paper · Sep 2, 2026
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How can an LLM decide when to ask clarifying questions before formulating an optimization model?When does an LLM’s verbal confidence reliably reflect its underlying uncertainty?How can we tell whether agreement among LLM judges reflects human alignment or shared blind spots?How can LLMs distinguish ambiguous inputs from gaps in their knowledge when estimating uncertainty?